A Deep Unfolding Framework for Diffractive Snapshot Spectral Imaging
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
Snapshot hyperspectral imaging systems acquire spectral data cubes through
compressed sensing. Recently, diffractive snapshot spectral imaging (DSSI)
methods have attracted significant attention. While various optical designs and
improvements continue to emerge, research on reconstruction algorithms remains
limited. Although numerous networks and deep unfolding methods have been
applied on similar tasks, they are not fully compatible with DSSI systems
because of their distinct optical encoding mechanism. In this paper, we propose
an efficient deep unfolding framework for diffractive systems, termed
diffractive deep unfolding (DDU). Specifically, we derive an analytical
solution for the data fidelity term in DSSI, ensuring both the efficiency and
the effectiveness during the iterative reconstruction process. Given the
severely ill-posed nature of the problem, we employ a network-based
initialization strategy rather than non-learning-based methods or linear
layers, leading to enhanced stability and performance. Our framework
demonstrates strong compatibility with existing state-of-the-art (SOTA) models,
which effectively address the initialization and prior subproblem. Extensive
experiments validate the superiority of the proposed DDU framework, showcasing
improved performance while maintaining comparable parameter counts and
computational complexity. These results suggest that DDU provides a solid
foundation for future unfolding-based methods in DSSI.
